The present invention relates generally to processing of an input image, especially from a video source, to improve spatial resolution for output to a high quality printing device. To improve the video signal spatial resolution for printing, the algorithm of the present invention applies an adaptive spatial interpolation technique to the video signal. The algorithm is based on a computer graphics curve-fitting algorithm with the addition of edge-restricted interpolation to sharpen edges and to enhance image details. Therefore, the new interpolation algorithm will not only improve the spatial resolution but also improve the sharpness of the printed image.
It has become increasingly important to get high quality prints from TV or other video signals in the desktop publishing and multi-media environment. Normally, a video signal has very low spatial resolution compared with the spatial resolution of printing devices. Hard copy from direct printing of a video signal is usually very poor in quality.
In order to produce a quality print of a video image, the resolution of the video signal must be increased to the resolution of the printing device. This increase in resolution must be accomplished by some interpolation algorithm. Interpolation of an image, whether video or otherwise, involves the estimation of pixel values located between known pixel values. Bilinear and high-order interpolations are generally preferred over the blocky pixel replication interpolation. However, these preferred interpolations result in a certain degree of blurring in the interpolated image. In order to preserve the sharpness of the image, its edge information should be considered in the interpolation process. In Miyake et al, "A New Interpolation Algorithm of Television Pictures for Compact Printing System," SPSE's 40th Annual Conference and Symposium on Hybrid Imaging Systems, pp. 17-20 (1987), dealing with the printing of video images, the interpolation was accomplished by convolution with a mask of coefficients. The mask employed a parameter that permitted the coefficients to be altered to change the appearance of the interpolated image. With the parameter equal to zero, the mask essentially provided a bilinear interpolation. However, the interpolation algorithm did not take into account edge information. In Tzou et al, "Subjectively Adapted Interpolation," Electronic Imaging '86, pp. 144-148 (1986), which did consider edge information, the interpolation algorithm used a variable interpolation filter which could be adjusted to any one of eight angle orientations. The orientation of the filter was adjusted throughout the interpolation process to the orientation most closely coinciding with the orientation of an edge present in the image. Since that algorithm only considered the edge information of eight angle orientations, the resulting image showed only moderate improvement over the bilinear interpolation.